Predicting poultry meat characteristics using an enhanced multi-target regression method

Santana, Everton José; Gerônimo, Bruna Caroline; Mastelini, Saulo Martiello; Carvalho, Rafael H.; Barbin, Douglas Fernandes; Ida, Elza Iouko; Barbon, Sylvio

doi:10.1016/j.biosystemseng.2018.04.023

Cited by 35 publications

(25 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The coefficient of determination was used to measure the goodness of fit between actual and predicted data. In general, if the coefficient of determination is 0.5≦, the accuracy of the regression analysis is considered to be good [ 41 ].…”

Section: Resultsmentioning

confidence: 99%

Central Sensitivity Is Associated with Poor Recovery of Pain: Prediction, Cluster, and Decision Tree Analyses

Shigetoh

Koga

Tanaka

et al. 2020

Pain Research and Management

View full text Add to dashboard Cite

The process of pain recovery varies and can include the recovery, maintenance, or worsening of symptoms. Many cases of patients with pain show a tendency of recovering as predicted; however, some do not. The characteristics of cases that do not fit the prediction of pain recovery remain unclear. We performed cluster and decision tree analyses to reveal the characteristics in cases that do not fit the prediction of pain recovery. A total of 43 patients with musculoskeletal pain (nonoperative: 22 patients, operative: 13 patients) and central pain (brain disease: 5 patients, spinal cord disease: 3 patients) were included in this longitudinal study. Central sensitivity syndrome (CSS) outcome measures (Central Sensitisation Inventory), pain intensity-related outcome measures (Short-Form McGill Pain Questionnaire-2 (SFMPQ-2)), and cognitive-emotional outcome measures (Hospital Anxiety and Depression Scale and Pain Catastrophising Scale-4) of all patients were assessed at baseline and after 1-2 months. Regression analysis was used to calculate pain recovery prediction values. A hierarchical cluster analysis based on the predicted change of SFMPQ-2 and the observed change of SFMPQ-2 was used to extract subgroups that fit and those that do not fit pain recovery prediction. To extract the characteristics of subgroups that do not fit the prediction of pain recovery, a decision tree analysis was performed. The level of significance was set at 5%. In the results of cluster analysis, patients were classified into three subgroups. Cluster 1 was characterised by worse pain intensity from baseline, cluster 2 by pain, having recovered less and mildly than the predicted value, and Cluster 3 by a marked recovery of pain. In the results of the decision tree analysis, the CSI change was extracted as an indicator related to the classification of all clusters. Our findings suggest that the poor improvement of CSS is characteristic in cases that do not fit the prediction of pain recovery.

show abstract

Section: Resultsmentioning

confidence: 99%

Central Sensitivity Is Associated with Poor Recovery of Pain: Prediction, Cluster, and Decision Tree Analyses

Shigetoh

Koga

Tanaka

et al. 2020

Pain Research and Management

View full text Add to dashboard Cite

show abstract

“…This strategy is also going to be used in this work. [Santana et al 2018] proposed the Multi-target Augment Stacking method as an application of the traditional ST stacking ensemble approach in MTR problems. In their method, an arbitrary number of regression techniques are used to generate the base models, instead of just one as in SST.…”

Section: Related Workmentioning

confidence: 99%

Stock Portfolio Prediction by Multi-Target Decision Support

Santana

Silva

Mastelini

et al. 2019

iSys

View full text Add to dashboard Cite

Investing in the stock market is a complex process due to its high volatility caused by factors as exchange rates, political events, inflation and the market history. To support investor's decisions, the prediction of future stock price and economic metrics is valuable. With the hypothesis that there is a relation among investment performance indicators, the goal of this paper was exploring multi-target regression (MTR) methods to estimate 6 different indicators and finding out the method that would best suit in an automated prediction tool for decision support regarding predictive performance. The experiments were based on 4 datasets, corresponding to 4 different time periods, composed of 63 combinations of weights of stock-picking concepts each, simulated in the US stock market. We compared traditional machine learning approaches with seven state-of-the-art MTR solutions: Stacked Single Target, Ensemble of Regressor Chains, Deep Structure for Tracking Asynchronous Regressor Stacking, Deep Regressor Stacking, Multi-output Tree Chaining, Multi-target Augment Stacking and Multi-output Random Forest (MORF). With the exception of MORF, traditional approaches and the MTR methods were evaluated with Extreme Gradient Boosting, Random Forest and Support Vector Machine regressors. By means of extensive experimental evaluation, our results showed that the most recent MTR solutions can achieve suitable predictive performance, improving all the scenarios (14.70% in the best one, considering all target variables and periods). In this sense, MTR is a proper strategy for building stock market decision support system based on prediction models.

show abstract

“…Portable NIR (450-950 nm) was successfully applied for predicting pH values in chicken breast. 18 Recently, Santana et al 19 studied the possibility to use portable NIR (1150-2150 nm) for predicting some chicken meat quality parameters. The best performance was reached for SVM-based cooking loss model (R 2 P ¼ 0.88).…”

Section: Scientific Research On Portable/handheld Nir Applications Fomentioning

confidence: 99%

Present and future of portable/handheld near-infrared spectroscopy in chicken meat industry

et al. 2019

View full text Add to dashboard Cite

Near-infrared spectroscopy is widely applied for characterization of meat products. The improvement of handheld and portable equipment has allowed the development of on-line applications in the food processing industry. Recently, the poultry industry has benefited from the advancement of applications for identification of different characteristics, including chemical composition, identification of different parts and detection of anomalies. In this work, a review of recent applications of handheld and portable near-infrared equipment is presented, reporting the state of the art and providing insights about future applications.

show abstract

Predicting poultry meat characteristics using an enhanced multi-target regression method

Cited by 35 publications

References 18 publications

Central Sensitivity Is Associated with Poor Recovery of Pain: Prediction, Cluster, and Decision Tree Analyses

Central Sensitivity Is Associated with Poor Recovery of Pain: Prediction, Cluster, and Decision Tree Analyses

Stock Portfolio Prediction by Multi-Target Decision Support

Present and future of portable/handheld near-infrared spectroscopy in chicken meat industry

Contact Info

Product

Resources

About